scholarly journals A Protocol for Pollution Index, Source Identification, and Spatial Analysis of Heavy Metals in Top Soil

2020 ◽  
Author(s):  
Amir Mohammadi ◽  
Sepideh Nemati Mansour ◽  
Maryam Faraji ◽  
Ali Abdolahnejad ◽  
Ali Toolabi ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Toxic Metals ◽  
Source Identification ◽  
Contamination Factor ◽  
Pollution Index ◽  
Principal Component ◽  
Moran’S I ◽  
Moran's I ◽  
Top Soil ◽  
Z Scores

Introduction: This study aimed to assess a good protocol for the contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils. Materials and Methods: In the first step, samples were taken from top soil (30 cm) and the metals were extracted and detected with ICP-AES. In the second step, Enrichment Factor, Geoaccumulation Index, and Contamination Factor of metals were calculated to determine soil contamination degree. Furthermore, the principal component analysis and correlation between metals were conducted for source identification. Results: Spatial analysis, as an important section of the present protocol, was performed using Arc GIS, kriging, and Moran's I models. As results of Moran's I model showed, distribution pattern for Fe, As, Cd, Cu, Ni, Pb, and Zn were random (z-scores ranged from -1.17 to 1.09), indicatingthat these elements could be emitted from different potential sources. In Moran's model, spatial autocorrelation of each pollutant could be measured based on its value and location. Conclusion: The finding of this protocol can be used for extraction of contamination indexes, concentration, spatial analysis, and source identification of toxic metals in top soils.

scholarly journals Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

2012 ◽  
Vol 3 (1) ◽  
pp. 217
Author(s):  
Rokhana Dwi Bekti
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Moran’S I ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
Relationship Of ◽  
The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

scholarly journals Priestorová analýza dát z povrchového zberu na lokalite Vráble-Fidvár

2021 ◽  
Vol 6 (1-2) ◽  
pp. 35-50
Author(s):  
Dominik Drozd
Keyword(s):  
Spatial Analysis ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Interpolation Method ◽  
Objective Evaluation ◽  
Moran’S I ◽  
Sampled Data ◽  
Moran's I ◽  
Geostatistical Interpolation ◽  
Global And Local

The goal of this study is to introduce selected methods of spatial analysis and their contribution to evaluation of fieldwalking data. Spatial analysis encompasses various methods suitable for identification, objective evaluation and visualization of spatial patterns which are present in obtained data. This article primarily deals with sampled data, collected during a 2007 fieldwalking campaign. The dataset consisting of potsherds was spatially autocorrelated, using the global and local Moran’s I coefficient, which was used to identify clusters of finds. Spatial pattern of the settlement was visualised by geostatistical interpolation method – kriging.

scholarly journals Assessment of Ecological Environment Quality in Kolkata Urban Agglomeration, India

2021 ◽  
Author(s):  
Sukamal Maity ◽  
Subhasis Das ◽  
Jhumarani Maity Pattanayak ◽  
Biswajit Bera ◽  
Pravat Kumar Shit
Keyword(s):  
Environmental Quality ◽  
Landsat Imagery ◽  
Urban Expansion ◽  
Good Condition ◽  
Principal Component ◽  
Urban Agglomeration ◽  
Moran’S I ◽  
Moran's I ◽  
Ecological Index ◽  
Novel Approach

Abstract The global ecosystem has been significantly disrupted on various spatiotemporal scales over the last three decades due to human activities. Geospatial technology can quickly, effectively, and quantitatively to evaluate the spatiotemporal change of eco-environmental quality (EEQ). The present study is focused on novel approach of Remote Sensing based Ecological Index (RSEI), using Landsat Imagery data to assess environmental conditions and changes pattern. Four ecological indicators were prepared in the year 1990, 2000, 2010 and 2020 of Kolkata urban agglomeration (KUA) to evaluate the ecological environmental condition. The principal component analysis (PCA) and spatial autocorrelation analysis can relate all indicators with each other’s and RSEI. Out study indicated, greenness and wetness have a positive effect on EEQ of the province, but both dryness and heat have a negative effect. However, it should be noted that greenness has a greater impact on the eco-environment than the other three indicators. Based on the RSEI values, we have categorized the environmental standards of the study area into four groups - very good (0.81 - 1.00), good (0.61 - 0.80), acceptable (0.41 - 0.60), poor (0.21 - 0.40), and very poor (0.00 - 0.20), where high values ​​indicate that environmental quality is stable and healthy for living organisms and low values ​​indicate relatively unstable and threatening conditions of the environment. The status of RSEI showed that 9.02%, 12.29%, 12.79% and 37.23% of an area was under poor to very poor condition in the year of 1990, 2000, 2010 and 2020 respectively. Good to very good condition of RSEI values was increased from 19.12% to 34.074% during 1990 to 2010, but declined of RSEI value 9.47% during 2010 to 2020 due to urban expansion. Here, Moran's I values fund that 0.265, 0.543, 0.396 and 0.367 in the year 1990, 2000, 2010 and 2020 respectively. The result of Moran’s I values indicate that clustering nature. The present study can helpful for the decision making of ecological management guided by planners and policy makers.

scholarly journals Assessment of elemental contamination in the bottom sediments from a dam reservoir using a sequential extraction technique and chemometric analysis

2013 ◽  
Vol 11 (12) ◽  
pp. 1981-1995 ◽  
Author(s):  
Marzena Dabioch ◽  
Andrzej Kita ◽  
Piotr Zerzucha ◽  
Katarzyna Pytlakowska
Keyword(s):  
Sequential Extraction ◽  
Bottom Sediments ◽  
Inductively Coupled Plasma ◽  
Contamination Factor ◽  
Pollution Index ◽  
Principal Component ◽  
Potential Ecological Risk ◽  
Emission Spectrometry ◽  
Degree Of Contamination ◽  
Risk Assessment Code

AbstractThe concentration of elements in sediments is an important aspect of the quality of water ecosystems. The element concentrations in bottom sediments from Goczalkowice Reservoir, Poland, were investigated to determine the levels, accumulation and distribution of elements; to understand the contamination and potential toxicity of elements; and to trace the possible source of pollution. Sediments were collected from 8 sampling points. The functional speciation, mobility and bioavailability of elements were evaluated by means of modified Tessier sequential extraction. The element contents were measured by optical emission spectrometry with inductively coupled plasma. The experimental results were analyzed using chemometric methods such as principal component analysis and cluster analysis to elucidate the metal distributions, correlations and associations. The highest concentrations of most elements were found at the center of the reservoir. The distribution of metals in the individual fractions was varied. To assess the extent of anthropogenic impact indices, contamination factor, degree of contamination, metal pollution index and risk assessment code were applied. The calculated factors showed the highest contamination factor and the ability of chromium to be released from sediments. The degree of contamination showed that the area is characterized by a very high contamination. Strontium and manganese showed high potential ecological risk for sediments.

Analisis Moran’s I, Geary’s C, dan Getis-Ord G pada Penerapan Jumlah Penderita Kusta di Kabupaten Gowa

2020 ◽  
Vol 2 (2) ◽  
pp. 151
Author(s):  
S. Sukarna ◽  
Wahidah Sanusi ◽  
Hafilah Hardiono
Keyword(s):  
Infectious Disease ◽  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Moran’S I ◽  
Analysis Method ◽  
Moran's I ◽  
Surrounding Areas ◽  
Geary’S C ◽  
Quadrat Analysis

Analisis spasial merupakan salah satu metode yang sering digunakan dalam melihat pola penyebaran penyakit menular. Penyakit Kusta atau lepra merupakan penyakit menular kronis yang disebabkan oleh bakteri Mycrobacterium Leprae yang penyebarannya melalui droplet. Penelitian ini bertujuan untuk mengetahui pola spasial pada Kusta dengan menggunakan metode Quadrat Analysis, untuk mengetahui ada atau tidaknya autokorelasi spasial antar daerah dengan menggunakan Moran’s I, Geary’s C, Getis-Ord G, dan pemetaan penyebaran penyakit Kusta di Kabupaten Gowa. Pada penelitian ini diperoleh bahwa pola spasial penyebaran penyakit Kusta pada Tahun 2016 dan 2017 di Kabupaten Gowa bersifat mengelompok (clustered). Pada Tahun 2016 terdapat autokorelasi spasial dengan pengujian Moran’s I  dan Geary’s C, sedangkan pengujian Getis-Ord G tidak terdapat autokorelasi spasial antar daerah. Pada Tahun 2017 tidak terdapat autokorelasi spasial antar daerah dengan menggunakan ke tiga pengujian tersebut. Pada Tahun 2016 daerah yang rawan adalah Barombong, daerah yang harus berhati-hati dengan daerah sekitarnya adalah Bontonompo dan daerah yang termasuk kategori aman adalah Tompobulu. Sedangkan pada tahun 2017 daerah yang rawan terhadap penyakit Kusta adalah Bajeng dan Manuju.Kata kunci : Moran’s I, Geary’s C, Getis-Ord G, Moran Scatterplot, Kusta Spatial analysis is one of the methods that is often used to observe spreading pattern of infectious diseases. Leprosy is a chronic infectious disease caused by bacterium Mycrobacterium Leprae which spreads through droplets. This study aims to determine the spatial pattern of leprosy using the Quadrat Analysis method, to determine whether there is spatial autocorrelation between regions using Moran's I, Geary’s C, Getis-Ord G, and mapping the spread of leprosy in Gowa Regency. In this study it was found that the spatial patterns of the spread of leprosy in 2016 and 2017 in Gowa Regency was clustered. In 2016 there were spatial autocorrelations with the tests of Moran's I and Geary's C, while the testing of Getis-Ord G did not have spatial autocorrelation between regions. In 2017 there is no spatial autocorrelation between regions using the three tests. In 2016 the vulnerable areas was Barombong, the area that had to be careful with the surrounding areas was Bontonompo and the area included in the safe category was Tompobulu. Whereas in 2017 areas prone to leprosy were Bajeng and Manuju.Keywords : Moran's I, Geary's C, Getis-Ord G, Moran Scatterplot, Leprosy

scholarly journals Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India

2014 ◽  
Vol II-8 ◽  
pp. 1-7 ◽  
Author(s):  
S. Verma ◽  
R. D. Gupta
Keyword(s):  
Spatial Analysis ◽  
Japanese Encephalitis ◽  
Temporal Dynamics ◽  
Uttar Pradesh ◽  
Public Health Problem ◽  
Moran’S I ◽  
Spatial And Temporal Variation ◽  
Spatiotemporal Pattern ◽  
Moran's I ◽  
Incidence Data

In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Matheus Supriyanto Rumetna ◽  
Eko Sediyono ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Land Use Changes ◽  
Geographical Information ◽  
Moran’S I ◽  
Complete Spatial Randomness ◽  
Moran's I ◽  
Global Moran’S I ◽  
Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined. 

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of ◽  
Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% ofneighbourhoods, area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

Distribution and Contamination Status of Heavy Metals in the Surface Sediments along Western Coast of Bali Strait, Banyuwangi

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Defri Yona ◽  
Syarifah Hikmah Julinda Sari ◽  
Anedathama Kretarta ◽  
Citra Ravena Putri Effendy ◽  
Misba Nur Aini ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Contamination Factor ◽  
Pollution Index ◽  
Western Coast ◽  
Sampling Locations ◽  
Accumulation Index ◽  
Geo Accumulation Index ◽  
Geo Accumulation

This study attempted to analyze the distribution and contamination status of heavy metals (Cu, Fe and Zn) along western coast of Bali Strait in Banyuwangi, East Java. Bali Strait is one of the many straits in Indonesia with high fisheries activities that could potentially contributed to high heavy metal pollution. There were five sampling areas from the north to south: Pantai Watu Dodol, Pantai Kalipuro, Ketapang Port, Pantai Boom and Muncar as the fish landing area. Heavy metal pollution in these locations comes from many different activities such as tourism, fish capture and fish industry and also domestic activities. Contamination factor (CF), geo-accumulation index (Igeo) and enrichment factor (EF) of each heavy metal were calculated to obtain contamination status of the research area. The concentrations of Fe were observed the highest (1.5-129.9 mg/kg) followed by Zn (13.2-23.5 mg/kg) and Cu (2.2-7.8 mg/kg). The distribution of Cu, Fe and Zn showed variability among the sampling locations in which high concentrations of Cu and Zn were higher in Ketapang Port, whereas high concentration of Fe was high in almost all sampling locations. According to the pollution index, contamination factors of Cu, Fe and Zn were low (CF < 1 and Igeo < 1). However, high index of EF (> 50) showed high influence of the anthropogenic activities to the contribution of the metals to the environment. This could also because of the high background value used in the calculation of the index due to the difficulties in finding background value from the sampling areas.Keywords: heavy metals, pollution index, contamination factor, geo-accumulation index, Bali Strait

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